As information society develops, data volumes increase exponentially, and data compression is widely applied in various fields because of advantages that data compression can eliminate information redundancy and improve data transmission efficiency and storage resource usage.
In a data compression process, central processing unit (CPU) and memory resources need to be occupied, which negatively affects system performance to some extent. Data compressibility essentially depends on a redundancy characteristic of data itself, that is, not all data is suitable for compression. For example, existing studies indicate that compression of data having low redundancy causes higher computing overheads and a severe waste of system resources. Therefore, before data compression, technologies for determining whether data is suitable for compression become particularly important.
Currently, whether data is suitable for compression is determined according to an estimated compression ratio. However, current methods for calculating an estimated compression ratio have a disadvantage of relatively low accuracy.